Loading and saving data

Contents

• Loading and saving data

  • Loading files: the load function

    • Loading multiple files

  • Saving data to files

  • Supported formats

    • HSpy - HyperSpy’s HDF5 Specification

      • Extra saving arguments

    • NetCDF

    • MRC

    • MRCZ

      • Extra saving arguments

      • Example Usage

    • EMSA/MSA

      • Extra saving arguments

    • Ripple

    • Images

    • TIFF

    • Gatan Digital Micrograph

      • Extra loading arguments

    • EDAX TEAM SPD and SPC

      • Extra loading arguments for SPD file

      • Extra loading arguments for SPD and SPC files

    • FEI TIA SER and EMI

      • Extra loading arguments

    • SEMPER UNF binary format

    • Blockfile

    • DENS heater log

    • Bruker’s formats

      • Bruker composite file

        • Extra loading arguments

      • SPX format

    • EMD

      • EMD (NCEM)

      • EMD (Velox)

        • Extra loading arguments

    • Protochips log

  • Reading data generated by HyperSpy using other software packages

    • ImportRPL Digital Micrograph plugin

    • readHyperSpyH5 MATLAB Plugin

Loading files: the load function

HyperSpy can read and write to multiple formats (see Supported formats). To load data use the load() command. For example, to load the image ascent.jpg you can type:

>>> s = hs.load("ascent.jpg")

If the loading was successful, the variable s contains a generic BaseSignal, a Signal1D or an Signal2D.

Note

Note for python programmers: the data is stored in a numpy array in the data attribute, but you will not normally need to access it there.)

HyperSpy will try to guess the most likely data type for the corresponding file. However, you can force it to read the data as a particular data type by providing the signal keyword, which has to be one of: spectrum, image or EELS, e.g.:

>>> s = hs.load("filename", signal = "EELS")

Some file formats store some extra information about the data, which can be stored in “attributes”. If HyperSpy manages to read some extra information about the data it stores it in the original_metadata attribute. Also, it is possible that other information will be mapped by HyperSpy to a standard location where it can be used by some standard routines, the metadata attribute.

To print the content of the parameters simply:

>>> s.metadata

The original_metadata and metadata can be exported to text files using the export() method, e.g.:

>>> s.original_metadata.export('parameters')

Deprecated since version 1.2: memmap_dir and load_to_memory load() keyword arguments. Use lazy instead of load_to_memory. lazy makes memmap_dir unnecessary.

Almost all file readers support accessing the data without reading it to memory (see Supported formats for a list). This feature can be useful when analysing large files. To load a file without loading it to memory simply set lazy to True e.g.:

The units of the navigation and signal axes can be converted automatically during loading using the convert_units parameter. If True, the convert_to_units method of the axes_manager will be used for the conversion and if set to False, the units will not be converted. The default is False.

>>> s = hs.load("filename.hspy", lazy=True)

More details on lazy evaluation support in Working with big data.

Loading multiple files

Rather than loading files individually, several files can be loaded with a single command. This can be done by passing a list of filenames to the load functions, e.g.:

>>> s = hs.load(["file1.hspy", "file2.hspy"])

or by using shell-style wildcards

New in version 1.2.0: stack multi-signal files

By default HyperSpy will return a list of all the files loaded. Alternatively, HyperSpy can stack the data of the files contain data with exactly the same dimensions. If this is not the case an error is raised. If each file contains multiple (N) signals, N stacks will be created. Here, the numbers of signals per file must also match, or an error will be raised.

It is also possible to load multiple files with a single command without stacking them by passing the stack=False argument to the load function, in which case the function will return a list of objects, e.g.:

>>> ls
CL1.raw  CL1.rpl~  CL2.rpl  CL3.rpl  CL4.rpl  LL3.raw  shift_map-          SI3.npy
CL1.rpl  CL2.raw   CL3.raw  CL4.raw  hdf5/    LL3.rpl
>>> s = hs.load('*.rpl')
>>> s
[<EELSSpectrum, title: CL1, dimensions: (64, 64, 1024)>,
<EELSSpectrum, title: CL2, dimensions: (64, 64, 1024)>,
<EELSSpectrum, title: CL3, dimensions: (64, 64, 1024)>,
<EELSSpectrum, title: CL4, dimensions: (64, 64, 1024)>,
<EELSSpectrum, title: LL3, dimensions: (64, 64, 1024)>]
>>> s = hs.load('*.rpl', stack=True)
>>> s
<EELSSpectrum, title: mva, dimensions: (5, 64, 64, 1024)>

Saving data to files

To save data to a file use the save() method. The first argument is the filename and the format is defined by the filename extension. If the filename does not contain the extension the default format (HSpy - HyperSpy’s HDF5 Specification) is used. For example, if the s variable contains the BaseSignal that you want to write to a file, the following will write the data to a file called spectrum.hspy in the default HSpy - HyperSpy’s HDF5 Specification format:

>>> s.save('spectrum')

If instead you want to save in the Ripple write instead:

>>> s.save('spectrum.rpl')

Some formats take extra arguments. See the relevant subsection of Supported formats for more information.

Supported formats

Here is a summary of the different formats that are currently supported by HyperSpy. The “lazy” column specifies if lazy evaluation is supported.

Supported file formats

Format	Read	Write	lazy
Gatan’s dm3	Yes	No	Yes
Gatan’s dm4	Yes	No	Yes
FEI’s emi and ser	Yes	No	Yes
HDF5	Yes	Yes	Yes
Image: jpg	Yes	Yes	Yes
TIFF	Yes	Yes	Yes
MRC	Yes	No	Yes
MRCZ	Yes	Yes	Yes
EMSA/MSA	Yes	Yes	No
NetCDF	Yes	No	No
Ripple	Yes	Yes	Yes
SEMPER unf	Yes	Yes	Yes
Blockfile	Yes	Yes	Yes
DENS heater log	Yes	No	No
Bruker’s bcf	Yes	No	Yes
Bruker’s spx	Yes	No	No
EMD (NCEM)	Yes	Yes	Yes
EMD (Velox)	Yes	No	Yes
Protochips log	Yes	No	No
EDAX .spc and .spd	Yes	No	Yes

HSpy - HyperSpy’s HDF5 Specification

This is the default format and it is the only one that guarantees that no information will be lost in the writing process and that supports saving data of arbitrary dimensions. It is based on the HDF5 open standard. The HDF5 file format is supported by many applications. Part of the specification is documented in Metadata structure.

New in version 1.2: Enable saving HSpy files with the .hspy extension. Preveously only the .hdf5 extension was recognised.

Changed in version 1.3: The default extension for the HyperSpy HDF5 specification is now .hspy. The option to change the default is no longer present in preferences.

Only loading of HDF5 files following the HyperSpy specification are supported. Usually their extension is .hspy extension, but older versions of HyperSpy would save them with the .hdf5 extension. Both extensions are recognised by HyperSpy since version 1.2. However, HyperSpy versions older than 1.2 won’t recognise the .hspy extension. To workaround the issue when using old HyperSpy installations simply change the extension manually to .hdf5 or save directly the file using this extension by explicitly adding it to the filename e.g.:

>>> s = hs.signals.BaseSignal([0])
>>> s.save('test.hdf5')

When saving to hspy, all supported objects in the signal’s metadata is stored. This includes lists, tuples and signals. Please note that in order to increase saving efficiency and speed, if possible, the inner-most structures are converted to numpy arrays when saved. This procedure homogenizes any types of the objects inside, most notably casting numbers as strings if any other strings are present:

>>> # before saving:
>>> somelist
[1, 2.0, 'a name']
>>> # after saving:
['1', '2.0', 'a name']

The change of type is done using numpy “safe” rules, so no information is lost, as numbers are represented to full machine precision.

This feature is particularly useful when using get_lines_intensity() (see get lines intensity):

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.metadata.Sample.intensities = s.get_lines_intensity()
>>> s.save('EDS_spectrum.hspy')

>>> s_new = hs.load('EDS_spectrum.hspy')
>>> s_new.metadata.Sample.intensities
[<BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Al_Ka at 1.49 keV, dimensions: (|)>,
 <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: C_Ka at 0.28 keV, dimensions: (|)>,
 <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Cu_La at 0.93 keV, dimensions: (|)>,
 <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Mn_La at 0.63 keV, dimensions: (|)>,
 <BaseSignal, title: X-ray line intensity of EDS SEM Signal1D: Zr_La at 2.04 keV, dimensions: (|)>]

New in version 1.3.1: chunks keyword argument

By default, the data is saved in chunks that are optimised to contain at least one full signal. It is possible to customise the chunk shape using the chunks keyword. For example, to save the data with (20, 20, 256) chunks instead of the default (7, 7, 2048) chunks for this signal:

>>> s = hs.signals.Signal1D(np.random.random((100, 100, 2048)))
>>> s.save("test_chunks", chunks=(20, 20, 256), overwrite=True)

Note that currently it is not possible to pass different customised chunk shapes to all signals and arrays contained in a signal and its metadata. Therefore, the value of chunks provided on saving will be applied to all arrays contained in the signal.

By passing True to chunks the chunk shape is guessed using h5py’s guess_chunks function what, for large signal spaces usually leads to smaller chunks as guess_chunks does not impose the constrain of storing at least one signal per chunks. For example, for the signal in the example above passing chunks=True results in (7, 7, 256) chunks.

Extra saving arguments

• compression : One of None, ‘gzip’, ‘szip’, ‘lzf’ (default is ‘gzip’).

NetCDF

This was the default format in HyperSpy’s predecessor, EELSLab, but it has been superseded by HDF5 in HyperSpy. We provide only reading capabilities but we do not support writing to this format.

Note that only NetCDF files written by EELSLab are supported.

To use this format a python netcdf interface must be installed manually because it is not installed by default when using the automatic installers.

MRC

This is a format widely used for tomographic data. Our implementation is based on this specification. We also partly support FEI’s custom header. We do not provide writing features for this format, but, as it is an open format, we may implement this feature in the future on demand.

For mrc files load takes the mmap_mode keyword argument enabling loading the file using a different mode (default is copy-on-write) . However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).

MRCZ

MRCZ is an extension of the CCP-EM MRC2014 file format. CCP-EM MRC2014 file format. It uses the blosc meta-compression library to bitshuffle and compress files in a blocked, multi-threaded environment. The supported data types are:

[float32,`int8`,`uint16`,`int16`,`complex64`]

It supports arbitrary meta-data, which is serialized into JSON.

MRCZ also supports asychronous reads and writes.

Repository: https://github.com/em-MRCZ PyPI: https://pypi.python.org/pypi/mrcz Citation: Submitted. Preprint: http://www.biorxiv.org/content/early/2017/03/13/116533

Support for this format is not enabled by default. In order to enable it install the mrcz and optionally the blosc Python packages.

Extra saving arguments

do_async:

currently supported within Hyperspy for writing only, this will save the file in a background thread and return immediately. Defaults to False.

Warning

There is no method currently implemented within Hyperspy to tell if an asychronous write has finished.

compressor:

The compression codec, one of [None,`’zlib`’,`’zstd’, `’lz4’]. Defaults to None.

clevel:

The compression level, an int from 1 to 9. Defaults to 1.

n_threads:

The number of threads to use for blosc compression. Defaults to the maximum number of virtual cores (including Intel Hyperthreading) on your system, which is recommended for best performance. If do_asyc = True you may wish to leave one thread free for the Python GIL.

The recommended compression codec is ‘zstd’ (zStandard) with clevel=1 for general use. If speed is critical, use ‘lz4’ (LZ4) with clevel=9. Integer data compresses more redably than floating-point data, and in general the histogram of values in the data reflects how compressible it is.

To save files that are compatible with other programs that can use MRC such as GMS, IMOD, Relion, MotionCorr, etc. save with compressor=None, extension .mrc. JSON metadata will not be recognized by other MRC-supporting software but should not cause crashes.

Example Usage

>>> s.save('file.mrcz', do_async=True, compressor='zstd', clevel=1)

>>> new_signal = hs.load('file.mrcz')

EMSA/MSA

This open standard format is widely used to exchange single spectrum data, but it does not support multidimensional data. It can be used to exchange single spectra with Gatan’s Digital Micrograph.

Warning

If several spectra are loaded and stacked (hs.load('pattern', stack_signals=True) the calibration read from the first spectrum and applied to all other spectra.

Extra saving arguments

For the MSA format the format argument is used to specify whether the energy axis should also be saved with the data. The default, ‘Y’ omits the energy axis in the file. The alternative, ‘XY’, saves a second column with the calibrated energy data. It is possible to personalise the separator with the separator keyword.

Warning

However, if a different separator is chosen the resulting file will not comply with the MSA/EMSA standard and HyperSpy and other software may not be able to read it.

The default encoding is latin-1. It is possible to set a different encoding using the encoding argument, e.g.:

>>> s.save('file.msa', encoding = 'utf8')

Ripple

This open standard format is widely used to exchange multidimensional data. However, it only supports data of up to three dimensions. It can be used to exchange data with Bruker and Lispix. Used in combination with the ImportRPL Digital Micrograph plugin it is very useful for exporting data to Gatan’s Digital Micrograph.

The default encoding is latin-1. It is possible to set a different encoding using the encoding argument, e.g.:

>>> s.save('file.rpl', encoding = 'utf8')

For mrc files load takes the mmap_mode keyword argument enabling loading the file using a different mode (default is copy-on-write) . However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).

Images

HyperSpy is able to read and write data too all the image formats supported by the Python Image Library (PIL). This includes png, pdf, gif etc.

It is important to note that these image formats only support 8-bit files, and therefore have an insufficient dynamic range for most scientific applications. It is therefore highly discouraged to use any general image format (with the exception of TIFF which uses another library) to store data for analysis purposes.

TIFF

HyperSpy can read and write 2D and 3D TIFF files using using Christoph Gohlke’s tifffile library. In particular it supports reading and writing of TIFF, BigTIFF, OME-TIFF, STK, LSM, NIH, and FluoView files. Most of these are uncompressed or losslessly compressed 2**(0 to 6) bit integer,16, 32 and 64-bit float, grayscale and RGB(A) images, which are commonly used in bio-scientific imaging. See the library webpage for more details.

Currently HyperSpy has limited support for reading and saving the TIFF tags. However, the way that HyperSpy reads and saves the scale and the units of tiff files is compatible with ImageJ/Fiji and Gatan Digital Micrograph software. HyperSpy can also import the scale and the units from tiff files saved using FEI and Zeiss SEM software.

>>> # Force read image resolution using the x_resolution, y_resolution and
>>> # the resolution_unit of the tiff tags. Be aware, that most of the
>>> # software doesn't (properly) use these tags when saving tiff files.
>>> s = hs.load('file.tif', force_read_resolution=True)

HyperSpy can also read and save custom tags through Christoph Gohlke’s tifffile library. See the library webpage for more details.

>>> # Saving the string 'Random metadata' in a custom tag (ID 65000)
>>> extratag = [(65000, 's', 1, "Random metadata", False)]
>>> s.save('file.tif', extratags=extratag)

>>> # Saving the string 'Random metadata' from a custom tag (ID 65000)
>>> s2 = hs.load('file.tif')
>>> s2.original_metadata['Number_65000']
b'Random metadata'

Gatan Digital Micrograph

HyperSpy can read both dm3 and dm4 files but the reading features are not complete (and probably they will be unless Gatan releases the specifications of the format). That said, we understand that this is an important feature and if loading a particular Digital Micrograph file fails for you, please report it as an issue in the issues tracker to make us aware of the problem.

Extra loading arguments

optimize: bool, default is True. During loading, the data is replaced by its optimized copy to speed up operations, e. g. iteration over navigation axes. The cost of this speed improvement is to double the memory requirement during data loading.

EDAX TEAM SPD and SPC

HyperSpy can read both .spd (spectrum image) and .spc (single spectra) files from the EDAX TEAM software. If reading an .spd file, the calibration of the spectrum image is loaded from the corresponding .ipr and .spc files stored in the same directory, or from specific files indicated by the user. If these calibration files are not available, the data from the .spd file will still be loaded, but with no spatial or energy calibration. If elemental information has been defined in the spectrum image, those elements will automatically be added to the signal loaded by HyperSpy.

Currently, loading an EDAX TEAM spectrum or spectrum image will load an EDSSEMSpectrum Signal. If support for TEM EDS data is needed, please open an issue in the issues tracker to alert the developers of the need.

For further reference, file specifications for the formats are available publicly available from EDAX and are on Github (.spc, .spd, and .ipr).

Extra loading arguments for SPD file

• spc_fname : {None, str}, name of file from which to read the spectral calibration. If data was exported fully from EDAX TEAM software, an .spc file with the same name as the .spd should be present. If None, the default filename will be searched for. Otherwise, the name of the .spc file to use for calibration can be explicitly given as a string.

• ipr_fname : {None, str}, name of file from which to read the spatial calibration. If data was exported fully from EDAX TEAM software, an .ipr file with the same name as the .spd (plus a “_Img” suffix) should be present. If None, the default filename will be searched for. Otherwise, the name of the .ipr file to use for spatial calibration can be explicitly given as a string.

• **kwargs: remaining arguments are passed to the Numpy memmap function.

Extra loading arguments for SPD and SPC files

• load_all_spc : bool, switch to control if all of the .spc header is read, or just the important parts for import into HyperSpy.

FEI TIA SER and EMI

HyperSpy can read ser and emi files but the reading features are not complete (and probably they will be unless FEI releases the specifications of the format). That said we know that this is an important feature and if loading a particular ser or emi file fails for you, please report it as an issue in the issues tracker to make us aware of the problem.

HyperSpy (unlike TIA) can read data directly from the .ser files. However, by doing so, the information that is stored in the emi file is lost. Therefore strongly recommend to load using the .emi file instead.

When reading an .emi file if there are several .ser files associated with it, all of them will be read and returned as a list.

Extra loading arguments

• only_valid_data : bool, in case of series or linescan data with the

acquisition stopped before the end: if True, load only the acquired data. If False, the empty data are filled with zeros. The default is False and this default value will change to True in version 2.0.

SEMPER UNF binary format

SEMPER is a fully portable system of programs for image processing, particularly suitable for applications in electron microscopy developed by Owen Saxton (see DOI: 10.1016/S0304-3991(79)80044-3 for more information).The unf format is a binary format with an extensive header for up to 3 dimensional data. HyperSpy can read and write unf-files and will try to convert the data into a fitting BaseSignal subclass, based on the information stored in the label. Currently version 7 of the format should be fully supported.

Blockfile

HyperSpy can read and write the blockfile format from NanoMegas ASTAR software. It is used to store a series of diffraction patterns from scanning precession electron diffraction (SPED) measurements, with a limited set of metadata. The header of the blockfile contains information about centering and distortions of the diffraction patterns, but is not applied to the signal during reading. Blockfiles only support data values of type np.uint8 (integers in range 0-255).

Warning

While Blockfiles are supported, it is a proprietary format, and future versions of the format might therefore not be readable. Complete interoperability with the official software can neither be guaranteed.

Blockfiles are by default loaded in a “copy-on-write” manner using numpy.memmap . For blockfiles load takes the mmap_mode keyword argument enabling loading the file using a different mode. However, note that lazy loading does not support in-place writing (i.e lazy loading and the “r+” mode are incompatible).

DENS heater log

HyperSpy can read heater log format for DENS solution’s heating holder. The format stores all the captured data for each timestamp, together with a small header in a plain-text format. The reader extracts the measured temperature along the time axis, as well as the date and calibration constants stored in the header.

Bruker’s formats

Bruker’s Esprit(TM) software and hardware allows to acquire and save the data in different kind of formats. Hyperspy can read two main basic formats: bcf and spx.

Bruker composite file

HyperSpy can read “hypermaps” saved with Bruker’s Esprit v1.x or v2.x in bcf hybrid (virtual file system/container with xml and binary data, optionally compressed) format. Most bcf import functionality is implemented. Both high-resolution 16-bit SEM images and hyperspectral EDX data can be retrieved simultaneously.

BCF can look as all inclusive format, however it does not save some key EDX parameters: any of dead/live/real times, FWHM at Mn_Ka line. However, real time for whole map is calculated from pixelAverage, lineAverage, pixelTime, lineCounter and map height parameters.

Note that Bruker Esprit uses a similar format for EBSD data, but it is not currently supported by HyperSpy.

Extra loading arguments

• select_type : one of (None, ‘spectrum’, ‘image’). If specified, only the corresponding type of data, either spectrum or image, is returned. By default (None), all data are loaded.

• index : one of (None, int, “all”). Allow to select the index of the dataset in the bcf file, which can contains several datasets. Default None value result in loading the first dataset. When set to ‘all’, all available datasets will be loaded and returned as separate signals.

• downsample : the downsample ratio of hyperspectral array (height and width only), can be integer >=1, where ‘1’ results in no downsampling (default 1). The underlying method of downsampling is unchangeable: sum. Differently than block_reduce from skimage.measure it is memory efficient (does not creates intermediate arrays, works inplace).

• cutoff_at_kV : if set (can be int or float >= 0) can be used either to crop or enlarge energy (or channels) range at max values (default None).

Example of loading reduced (downsampled, and with energy range cropped) “spectrum only” data from bcf (original shape: 80keV EDS range (4096 channels), 100x75 pixels):

>>> hs.load("sample80kv.bcf", select_type='spectrum', downsample=2, cutoff_at_kV=10)
<EDSSEMSpectrum, title: EDX, dimensions: (50, 38|595)>

load the same file without extra arguments:

>>> hs.load("sample80kv.bcf")
[<Signal2D, title: BSE, dimensions: (|100, 75)>,
<Signal2D, title: SE, dimensions: (|100, 75)>,
<EDSSEMSpectrum, title: EDX, dimensions: (100, 75|1095)>]

The loaded array energy dimension can by forced to be larger than the data recorded by setting the ‘cutoff_at_kV’ kwarg to higher value:

>>> hs.load("sample80kv.bcf", cutoff_at_kV=80)
[<Signal2D, title: BSE, dimensions: (|100, 75)>,
<Signal2D, title: SE, dimensions: (|100, 75)>,
<EDSSEMSpectrum, title: EDX, dimensions: (100, 75|4096)>]

Note that setting downsample to >1 currently locks out using SEM imagery as navigator in the plotting.

SPX format

Hyperspy can read Bruker’s spx format (single spectra format based on XML). The format contains extensive list of details and parameters of EDS analyses which are mapped in hyperspy to metadata and original_metadata dictionaries.

EMD

EMD stands for “Electron Microscopy Dataset.” It is a subset of the open source HDF5 wrapper format. N-dimensional data arrays of any standard type can be stored in an HDF5 file, as well as tags and other metadata.

EMD (NCEM)

This EMD format was developed by Colin Ophus at the National Center for Electron Microscopy (NCEM). See http://emdatasets.com/ for more information.

For files containing several datasets, the dataset_name argument can be used to select a specific one:

>>> s = hs.load("adatafile.emd", dataset_name="/experimental/science_data_1")

Or several by using a list:

>>> s = hs.load("adatafile.emd",
...             dataset_name=[
...                 "/experimental/science_data_1",
...                 "/experimental/science_data_1"])

asdf

EMD (Velox)

This is a non-compliant variant of the standard EMD format developed by Thermo-Fisher (former FEI). HyperSpy supports importing images, EDS spectrum and EDS spectrum streams (spectrum images stored in a sparse format). For spectrum streams, there are several loading options (described below) to control the frames and detectors to load and if to sum them on loading. The default is to import the sum over all frames and over all detectors in order to decrease the data size in memory.

Note

Pruned Velox EMD files only contain the spectrum image in a proprietary format that HyperSpy cannot read. Therefore, don’t prune FEI EMD files in you intend to read them with HyperSpy.

>>> hs.load("sample.emd")
[<Signal2D, title: HAADF, dimensions: (|179, 161)>,
<EDSSEMSpectrum, title: EDS, dimensions: (179, 161|4096)>]

Note

Currently only lazy uncompression rather than lazy loading is implemented. This means that it is not currently possible to read EDS SI Veloz EMD files with size bigger than the available memory.

Note

Loading a spectrum image can be slow if numba is not installed.

Warning

This format is still not stable and files generated with the most recent version of Velox may not be supported. If you experience issues loading a file, please report it to the HyperSpy developers so that they can add support for newer versions of the format.

Extra loading arguments

• select_type : one of {None, ‘image’, ‘single_spectrum’, ‘spectrum_image’} (default is None).

• first_frame : integer (default is 0).

• last_frame : integer (default is None)

• sum_frames : boolean (default is True)

• sum_EDS_detectors : boolean (default is True)

• rebin_energy : integer (default is 1)

• SI_dtype : numpy dtype (default is None)

• load_SI_image_stack : boolean (default is False)

The select_type parameter specifies the type of data to load: if image is selected, only images (including EDS maps) are loaded, if single_spectrum is selected, only single spectra are loaded and if spectrum_image is selected, only the spectrum image will be loaded. The first_frame and last_frame parameters can be used to select the frame range of the EDS spectrum image to load. To load each individual EDS frame, use sum_frames=False and the EDS spectrum image will be loaded with an an extra navigation dimension corresponding to the frame index (time axis). Use the sum_EDS_detectors=True parameter to load the signal of each individual EDS detector. In such a case, a corresponding number of distinct EDS signal is returned. The default is sum_EDS_detectors=True, which loads the EDS signal as a sum over the signals from each EDS detectors. The rebin_energy and SI_dtype parameters are particularly useful in combination with sum_frames=False to reduce the data size when one want to read the individual frames of the spectrum image. If SI_dtype=None (default), the dtype of the data in the emd file is used. The load_SI_image_stack parameter allows loading the stack of STEM images acquired simultaneously as the EDS spectrum image. This can be useful to monitor any specimen changes during the acquisition or to correct the spatial drift in the spectrum image by using the STEM images.

>>> hs.load("sample.emd", sum_EDS_detectors=False)
[<Signal2D, title: HAADF, dimensions: (|179, 161)>,
<EDSSEMSpectrum, title: EDS - SuperXG21, dimensions: (179, 161|4096)>,
<EDSSEMSpectrum, title: EDS - SuperXG22, dimensions: (179, 161|4096)>,
<EDSSEMSpectrum, title: EDS - SuperXG23, dimensions: (179, 161|4096)>,
<EDSSEMSpectrum, title: EDS - SuperXG24, dimensions: (179, 161|4096)>]

>>> hs.load("sample.emd", sum_frames=False, load_SI_image_stack=True, SI_dtype=np.int8, rebin_energy=4)
[<Signal2D, title: HAADF, dimensions: (50|179, 161)>,
<EDSSEMSpectrum, title: EDS, dimensions: (50, 179, 161|1024)>]

Protochips log

HyperSpy can read heater, biasing and gas cell log files for Protochips holder. The format stores all the captured data together with a small header in a csv file. The reader extracts the measured quantity (e. g. temperature, pressure, current, voltage) along the time axis, as well as the notes saved during the experiment. The reader returns a list of signal with each signal corresponding to a quantity. Since there is a small fluctuation in the step of the time axis, the reader assumes that the step is constant and takes its mean, which is a good approximation. Further release of HyperSpy will read the time axis more precisely by supporting non-linear axis.

Reading data generated by HyperSpy using other software packages

The following scripts may help reading data generated by HyperSpy using other software packages.

ImportRPL Digital Micrograph plugin

This Digital Micrograph plugin is designed to import Ripple files into Digital Micrograph. It is used to ease data transit between DigitalMicrograph and HyperSpy without losing the calibration using the extra keywords that HyperSpy adds to the standard format.

When executed it will ask for 2 files:

1. The riple file with the data format and calibrations

2. The data itself in raw format.

If a file with the same name and path as the riple file exits with raw or bin extension it is opened directly without prompting

ImportRPL was written by Luiz Fernando Zagonel.

Download ImportRPL

readHyperSpyH5 MATLAB Plugin

This MATLAB script is designed to import HyperSpy’s saved HDF5 files (.hspy extension). Like the Digital Micrograph script above, it is used to easily transfer data from HyperSpy to MATLAB, while retaining spatial calibration information.

Download readHyperSpyH5 from its Github repository.